• Develop novel microservices for smart control of natural ventilation and TABS • Microservices incorporate IoT network API and Edge computing Weather Forecast API • Machine learning algorithms effectively maintain indoor CO2 below 800-900 ppm • Augmented TABS control saves heating energy and prevents overheating (<26°C) • Microservices enable scalable resilient IoT frameworks for energy-efficient buildings Smart buildings often struggle with the automatic control of complex heating, ventilation, and air conditioning systems, especially natural ventilation control. This paper introduces a novel microservices architecture to enable machine learning (ML) algorithms for the natural ventilation control experiments in smart buildings. Implemented and evaluated in a three-story smart building in Cambridge, MA, from 2019 to 2021, the architecture incorporates a Python-based IoT network API and a Weather Forecast API. Experimental research demonstrated that predictive and reinforcement learning algorithms effectively controlled NV, optimizing CO 2 levels (800-900 ppm) and indoor air temperature (below 26°C). Additionally, augmented TABS control, leveraging solar radiation prediction, successfully prevented overheating and saved heating energy. This study highlights the critical importance of microservices architecture in transforming complex building systems into scalable, resilient IoT frameworks for control research, enabling advanced ML for more climate-responsive and energy-efficient buildings.
Zhang et al. (Sun,) studied this question.